ICRA 2012 Paper Abstract


Paper WeD210.1

Tick, David (University of Texas at Dallas), Rahman, Tauhidur (University of Texas at Dallas), Busso, Carlos (University of Texas at Dallas), Gans, Nicholas (University Texas at Dallas)

Indoor Robotic Terrain Classification Via Angular Velocity Based Hierarchical Classifier Selection

Scheduled for presentation during the Interactive Session "Interactive Session WeD-2" (WeD210), Wednesday, May 16, 2012, 17:00−17:30, Ballroom D

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on June 20, 2018

Keywords Force and Tactile Sensing, Wheeled Robots


This paper proposes a novel approach to terrain classification by wheeled mobile robots, which utilizes vibration data. In our proposed approach, a mobile robot has the ability to categorize terrain types simply by driving over them. Classification of terrain is based on measurements obtained from an inertial measurement unit strapped directly to the robotís chassis. In contrast to the previous approaches, we use acceleration and angular velocity measurements in all cardinal directions to extract over 800 features. Sequential Forward Floating Feature Selection is used to narrow down this large group of features to a set of 15 to 20 that are the most useful. The reduced set of features is used by a Linear Bayes Normal Classifier to classify terrain. Furthermore, different feature sets are generated for different velocity conditions, and the classifier switches based on the current robot velocity. Experimental results are presented that show the strong performance of the proposed system, including 90% accuracy over 20 continuous minutes of driving across different terrains.



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